SoroushVahidi/scidocs huggingface dataset:
Pellet: An OWL DL Reasoner
(2004)

Reasoning capability is of crucial importance to many applications developed for the Semantic Web. Description Logics provide sound and complete reasoning algorithms that can effectively handle the DL fragment of the Web Ontology Language (OWL). However, existing DL reasoners were implemented long before OWL came into existence and lack some features that are essential for Semantic Web applications, such as reasoning with individuals, querying capabilities, nominal support, elimination of the unique name assumption and so forth. With these objectives in mind we have implemented an OWL DL reasoner and deployed it in various kinds of applications.
Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders
(2018)

Normally, lesions are detected using supervised learning techniques that require labelled training data. We explore the use of Bayesian autoencoders to learn the variability of healthy tissue and detect lesions as unlikely events under the normative model. As a proof-of-concept, we test our method on registered 2D midaxial slices from CT imaging data. Our results indicate that our method achieves best performance in detecting lesions caused by bleeding compared to baselines.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
(2017)

While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity.
A 3D Face Model for Pose and Illumination Invariant Face Recognition
(2009)

Generative 3D face models are a powerful tool in computer vision. They provide pose and illumination invariance by modeling the space of 3D faces and the imaging process. The power of these models comes at the cost of an expensive and tedious construction process, which has led the community to focus on more easily constructed but less powerful models. With this paper we publish a generative 3D shape and texture model, the Basel Face Model (BFM), and demonstrate its application to several face recognition task. We improve on previous models by offering higher shape and texture accuracy due to a better scanning device and less correspondence artifacts due to an improved registration algorithm. The same 3D face model can be fit to 2D or 3D images acquired under different situations and with different sensors using an analysis by synthesis method. The resulting model parameters separate pose, lighting, imaging and identity parameters, which facilitates invariant face recognition across sensors and data sets by comparing only the identity parameters. We hope that the availability of this registered face model will spur research in generative models. Together with the model we publish a set of detailed recognition and reconstruction results on standard databases to allow complete algorithm comparisons.
Deep Learning Face Attributes in the Wild
(2015)

Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.
Learning from Simulated and Unsupervised Images through Adversarial Training
(2017)

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulators output using unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (i) a self-regularization term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.
A Measure of Polarization on Social Media Networks Based on Community Boundaries
(2013)

Polarization in social media networks is a fact in several scenarios such as political debates and other contexts such as same-sex marriage, abortion and gun control. Understanding and quantifying polarization is a longterm challenge to researchers from several areas, also being a key information for tasks such as opinion analysis. In this paper, we perform a systematic comparison between social networks that arise from both polarized and non-polarized contexts. This comparison shows that the traditional polarization metric – modularity – is not a direct measure of antagonism between groups, since non-polarized networks may be also divided into fairly modular communities. To bridge this conceptual gap, we propose a novel polarization metric based on the analysis of the boundary of a pair of (potentially polarized) communities, which better captures the notions of antagonism and polarization. We then characterize polarized and non-polarized social networks according to the concentration of high-degree nodes in the boundary of communities, and found that polarized networks tend to exhibit low concentration of popular nodes along the boundary. To demonstrate the usefulness of our polarization measures, we analyze opinions expressed on Twitter on the gun control issue in the United States, and conclude that our novel metrics help making sense of opinions expressed on online media.
Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography.
(2014)

OBJECTIVE
Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements.


APPROACH
Eight human subjects were asked to imagine either 'left' or 'right' hand movement during one recording session to control the computer cursor using TCRE and disc electrodes.


MAIN RESULTS
The obtained results show a significant improvement in accuracies using TCREs (44%-100%) compared to disc electrodes (30%-86%).


SIGNIFICANCE
This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.
Assessment of healthcare claims rejection risk using machine learning
(2017)

Modern healthcare service records, called Claims, record the medical treatments by a Provider (Doctor/Clinic), medication advised etc., along with the charges, and payments to be made by the patient and the Payer (insurance provider). Denial and rejection of healthcare claims is a significant administrative burden and source of loss to various healthcare providers and payers as well. Automating the identification of Claims prone to denial by reason, source, cause and other deciding factors is critical to lowering this burden of rework. We present classification methods based on Machine Learning (ML) to fully automate identification of such claims prone to rejection or denial with high accuracy, investigate the reasons for claims denial and recommend methods to engineer features using Claim Adjustment Reason Codes (CARC) as features with high Information Gain. The ML engine reported is first of its kind in Claims risk identification and represents a novel, significant enhancement to the state of practice of using ML for automating and containing claims denial risks.
A 16 Gb/s receiver with DC wander compensated rail-to-rail AC coupling and passive linear-equalizer in 22 nm CMOS
(2014)

A 16 Gb/s receiver implemented in 22 nm SOI CMOS technology is reported. The analog frontend accepts a rail-to-rail input common-mode imposed from the transmitter side. It consists of a baseline wander compensated passive linear equalizer that AC-couples the received signal to the subsequent active CTLE with a regulated common-mode level. The programmable passive linear equalizer features a frequency response suitable for low-frequency equalization such as for skin-effect losses. When its zero is programmed at 200 MHz minimum frequency, the measured maximum mid-band peaking is 7 dB. The receiver architecture is half-rate and comprises an 8-tap DFE and a baud-rate CDR. With no FFE at the transmitter, 0.9 Vppd PRBS31 NRZ data are recovered error-free (BER<;10-12) across a copper channel with 34 dB attenuation at 8 GHz.
A Passive Gait-Based Weight-Support Lower Extremity Exoskeleton With Compliant Joints
(2016)

This paper presents the design and analysis of a passive body weight (BW)-support lower extremity exoskeleton (LEE) with compliant joints to relieve compressive load in the knee. The biojoint-like mechanical knee decouples human gait into two phases, stance and swing, by a dual snap fit. The knee joint transfers the BW to the ground in the stance phases and is compliant to free the leg in the swing phases. Along with a leg dynamic model and a knee biomechanical model, the unmeasurable knee internal forces are simulated. The concept feasibility and dynamic models of the passive LEE design have been experimentally validated with measured plantar forces. The reduced knee forces confirm the effectiveness of the LEE in supporting human BW during walking and also provide a basis for computing the internal knee forces as a percentage of BW. Energy harvested from the hip spring reveals that the LEE can save human walking energy.
A Second-Order Class-D Audio Amplifier
(2011)

Class-D audio amplifiers are particularly efficient, and this efficiency has led to their ubiquity in a wide range of modern electronic appliances. Their output takes the form of a high-frequency square wave whose duty cycle (ratio of on-time to off-time) is modulated at low frequency according to the audio signal. A mathematical model is developed here for a second-order class-D amplifier design (i.e., containing one second-order integrator) with negative feedback. We derive exact expressions for the dominant distortion terms, corresponding to a general audio input signal, and confirm these predictions with simulations. We also show how the observed phenomenon of “pulse skipping” arises from an instability of the analytical solution upon which the distortion calculations are based, and we provide predictions of the circumstances under which pulse skipping will take place, based on a stability analysis. These predictions are confirmed by simulations.
A Compact GPS Antenna for Artillery Projectile Applications
(2011)

This letter presents a compact antenna suitable for the reception of GPS signals on artillery projectiles over 1.57-1.60 GHz. Four inverted-F-type elements are excited by a series feed network in equal magnitude and successive 90° phase difference. The shape and form factor of the antenna is tailored so that the antenna can be easily installed inside an artillery fuze. Measurements show that the proposed antenna has a gain of 2.90-3.77 dBic, an axial ratio of 1.9-2.86 dB, and a reflection coefficient of less than -10 dB over 1.57-1.62 GHz.
A Graph Rewriting Approach for Converting Asynchronous ROMs into Synchronous Ones
(2011)

Most of FPGAs have Configurable Logic Blocks (CLBs) to implement combinational and sequential circuits and block RAMs to implement Random Access Memories (RAMs) and Read Only Memories (ROMs). Circuit design that minimizes the number of clock cycles is easy if we use asynchronous read operations. However, most of FPGAs support synchronous read operations, but do not support asynchronous read operations. The main contribution of this paper is to provide one of the potent approaches to resolve this problem. We assume that a circuit using asynchronous ROMs designed by a non-expert or quickly designed by an expert is given. Our goal is to convert this circuit with asynchronous ROMs into an equivalent circuit with synchronous ones. The resulting circuit with synchronous ROMs can be embedded into FPGAs. We also discuss several techniques to decrease the latency and increase the clock frequency of the resulting circuits. key words: FPGA, block RAMs, asynchronous read operations, rewriting algorithm
Implementation of a Restricted Boltzmann Machine in a Spiking Neural Network
(2012)

Restricted Boltzmann Machines (RBMs) have been demonstrated to perform efficiently on a variety of applications, such as dimensionality reduction and classification. Implementing RBMs on neuromorphic hardware has certain advantages, particularly from a concurrency and lowpower perspective. This paper outlines some of the requirements involved for neuromorphic adaptation of an RBM and attempts to address these issues with suitably targeted modifications for sampling and weight updates. Results show the feasibility of such alterations which will serve as a guide for future implementation of such algorithms in VLSI arrays of spiking neurons.
Citation-based bootstrapping for large-scale author disambiguation
(2012)

We present a new, two-stage, self-supervised algorithm for author disambiguation in large bibliographic databases. In the first “bootstrap” stage, a collection of highprecision features is used to bootstrap a training set with positive and negative examples of coreferring authors. A supervised feature-based classifier is then trained on the bootstrap clusters and used to cluster the authors in a larger unlabeled dataset. Our selfsupervised approach shares the advantages of unsupervised approaches (no need for expensive hand labels) as well as supervised approaches (a rich set of features that can be discriminatively trained). The algorithm disambiguates 54,000,000 author instances in Thomson Reuters’ Web of Knowledge with B3 F1 of .807. We analyze parameters and features, particularly those from citation networks, which have not been deeply investigated in author disambiguation. The most important citation feature is self-citation, which can be approximated without expensive extraction of the full network. For the supervised stage, the minor improvement due to other citation features (increasing F1 from .748 to .767) suggests they may not be worth the trouble of extracting from databases that don’t already have them. A lean feature set without expensive abstract and title features performs 130 times faster with about equal F
The bright-side and the dark-side of CEO personality: examining core self-evaluations, narcissism, transformational leadership, and strategic influence.
(2009)

This article reports on an examination of the relationships between chief executive officer (CEO) personality, transformational and transactional leadership, and multiple strategic outcomes in a sample of 75 CEOs of Major League Baseball organizations over a 100-year period. CEO bright-side personality characteristics (core self-evaluations) were positively related to transformational leadership, whereas dark-side personality characteristics (narcissism) of CEOs were negatively related to contingent reward leadership. In turn, CEO transformational and contingent reward leadership were related to 4 different strategic outcomes, including manager turnover, team winning percentage, fan attendance, and an independent rating of influence. CEO transformational leadership was positively related to ratings of influence, team winning percentage, and fan attendance, whereas contingent reward leadership was negatively related to manager turnover and ratings of influence.
Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas
(2015)

Automatic methods for an early detection of plant diseases (i.e., visible symptoms at early stages of disease development) using remote sensing are critical for precision crop protection. Verticillium wilt (VW) of olive caused by Verticillium dahliae can be controlled only if detected at early stages of development. Linear discriminant analysis (LDA) and support vector machine (SVM) classification methods were applied to classify V. dahliae severity using remote sensing at large scale. High-resolution thermal and hyperspectral imagery were acquired with a manned platform which flew a 3000-ha commercial olive area. LDA reached an overall accuracy of 59.0% and a κ of 0.487 while SVM obtained a higher overall accuracy, 79.2% with a similar κ, 0.495. However, LDA better classified trees at initial and low severity levels, reaching accuracies of 71.4 and 75.0%, respectively, in comparison with the 14.3% and 40.6% obtained by SVM. Normalized canopy temperature, chlorophyll fluorescence, structural, xanthophyll, chlorophyll, carotenoid and disease indices were found to be the best indicators for early and advanced stage infection by VW. These results demonstrate that the methods developed in other studies at orchard scale are valid for flights in large areas comprising several olive orchards differing in soil and crop management characteristics.
EMDR: a putative neurobiological mechanism of action.
(2002)

Numerous studies have provided evidence for the efficacy of eye movement desensitization and reprocessing therapy (EMDR) in the treatment of posttraumatic stress disorder (PTSD), including recent studies showing it to be more efficient than therapist-directed flooding. But few theoretical explanations of how EMDR might work have been offered. Shapiro, in her original description of EMDR, proposed that its directed eye movements mimic the saccades of rapid eye movement sleep (REM), but provided no clear explanation of how such mimicry might lead to clinical improvement. We now revisit her original proposal and present a complete model for how EMDR could lead to specific improvement in PTSD and related conditions. We propose that the repetitive redirecting of attention in EMDR induces a neurobiological state, similar to that of REM sleep, which is optimally configured to support the cortical integration of traumatic memories into general semantic networks. We suggest that this integration can then lead to a reduction in the strength of hippocampally mediated episodic memories of the traumatic event as well as the memories' associated, amygdala-dependent, negative affect. Experimental data in support of this model are reviewed and possible tests of the model are suggested.
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses
(2015)

Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health – much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful.
Camouflage of self-inflicted razor blade incision scars with carbon dioxide laser resurfacing and thin skin grafting.
(2005)

BACKGROUND
Self-cutting using a razor blade is a type of self-mutilating behavior that leaves permanent and socially unacceptable scars with unique patterns, particularly on the upper extremities and anterior chest wall. These scars are easily recognized in the community and become a source of lifelong guilt, shame, and regret for the self-mutilators. In the presented clinical study, we aimed to investigate the effectiveness of carbon dioxide laser resurfacing and thin skin grafting in camouflaging self-inflicted razor blade incision scars.


METHODS
A total of 26 anatomical sites (11 upper arm, 11 forearm, and four anterior chest) of 16 white male patients, whose ages ranged from 20 to 41 years (mean, 23.8 years), were treated between February of 2001 and August of 2003. Detailed psychiatric evaluation preoperatively; informing the patient that the procedure is a "camouflage" operation; trimming hypertrophic scars down to intact skin level; intralesional corticosteroid injection to hypertrophic scars; carbon dioxide laser resurfacing as a single unit; thin (0.2 to 0.3 mm) skin grafting; compressive dressing for 15 days; use of tubular bandage; and protection from sunlight for at least 6 months constituted the key points of the procedure.


RESULTS
The scars were successfully camouflaged and converted to a socially acceptable appearance similar to a burn scar. Partial graft loss in one case and hyperpigmentation in another case were the complications. No new hypertrophic scar developed.


CONCLUSIONS
The carbon dioxide laser resurfacing and thin skin grafting method is effective in camouflaging self-inflicted razor blade incision scars.